Determining Collaboration Recommendations from File Path Information

3. The method of claim 2, wherein the predictive model is generated based at least in part on the one or more interaction attributes.

7. The method of claim 6, wherein processing the one or more recommended content objects includes at least one of scoring, sorting, ranking, filtering, decorating, or presenting the one or more recommended content objects.

11. The non-transitory computer readable medium of claim 10, wherein the predictive model is generated based at least in part on the interaction attributes.

12. The non-transitory computer readable medium of claim 10, wherein the directory hierarchy representation is codified in the vector, wherein the pathname having a form “/A/B/” for the content object is tokenized such that a first sub-directory of the directory hierarchy “A” forms the first file path token, and a second sub-directory of the directory hierarchy “B” forms the second file path token of the vector for the content object.

13. The non-transitory computer readable medium of claim 12, wherein the vectors are accessed to determine one or more recommended content objects comprising the recommended content object at least by determining, at the predicted model, one or more respective click probabilities of the one or more recommended content objects based at least in part upon one or more attributes of respective pathnames of the one or more recommended content objects, and information pertaining to the one or more recommended content objects is ordered based at least in part upon the one or more respective click probabilities.

15. The non-transitory computer readable medium of claim 14, wherein processing the one or more recommended content objects includes at least one of scoring, sorting, ranking, filtering, decorating, or presenting the one or more recommended content objects.